In May 2016, SSA & Company (SSA) convened 20 senior executives across a variety of industries – including consumer packaged goods, insurance, financial services, industrials, technology, and private equity – in Chicago to discuss big data leadership strategies. This luncheon is part of SSA’s Activate Analytics series: conversations designed to help business leaders harness the full potential of advanced analytics to drive efficiencies and innovation for growth. The discussion featured Deb Henretta, Former Group President of e-Business at Procter & Gamble (P&G) and SSA Senior Advisor, and Michael Schrage, research fellow at the MIT Sloan School’s Center for Digital Business. Jason Meil, Managing Director of New Products and Innovation at SSA, served as host and moderator.
Deb Henretta is one of the most successful Fortune 100 executives of the past decade. She spent 30 years working for A.G. Lafley at P&G, most recently as global president of e-business, where she also ran the $20B beauty business. Acting as the CEO for the P&G’s $23B Asia business, Deb embedded advanced analytics into the organization’s culture and decisionmaking – more than doubling the size of the business. Deb has been consistently recognized as an influential business leader, including seven consecutive years on Fortune magazine’s US and international rankings of the 50 Most Powerful Women in Business.
Michael Schrage is a research fellow at the MIT Sloan School’s Center for Digital Business, a columnist for Fortune, CIO Magazine, and MIT’s Technology Review, and consults to the U.S. government on national security systems innovation. Michael is the author of “Serious Play: How the World’s Best Companies Simulate to Innovate” and “What Do You Want Your Customers to Become?”
Today, growth has become more complex than ever. With declining global GDP, an emerging talent shortage, and a shortened timeframe to achieve results, CEOs and management teams look to the challenging but exciting opportunities provided by data and digital. Yet, despite investments in data analytics and new technologies, most companies have yet to see transformative results. SSA’s recent survey of nearly 50 senior executives at Fortune 500 companies (in NYC and Chicago) revealed that leaders rate their companies as just middle of the road in data maturity (average of 2.4 out of 4).
Below, we have captured six interesting ideas that emerged from the discussion.
1. Deliver a Return on Analytics (ROA) for innovation and growth.
- Leaders must ask themselves: How does this ever expanding resource of data directly or indirectly relate to achieving business growth?
- Innovation is the conversion of novelty into value; data is the spark for innovation and the enabler of its scale and value.
- Data and/or analytics can be used to: (1) inform or improve identification of a novelty from an insight; (2) drive the scalability of that novelty into a new product feature or service; and (3) improve the value of that product or service (user experience).
- Innovation happens at the edges and often, a need for growth or efficiencies prompts it.
- Disney Parks recognized an operations problem – how could the company increase park attendance without increasing wait times for attractions? Approaching this classic throughput challenge by using a combination of data, technology, people, and process, led to the development their innovative solution, the MagicBand. This new technology system increased the park’s capacity, decreased wait times, and improved the customer experience.
- Leaders can catalyze digital innovation by creating conditions that necessitate “turning a bug into a feature.”
- While at P&G, Deb stripped the budget of the ailing Old Spice business unit, forcing them to adopt digital. Compelled to think creatively and embrace new technologies, the team built a “living dashboard” that integrated business and consumer data.
- With new insights on what consumers share, the team purchased an ad spot in Alaska that happened to air the day of the Super Bowl and it went viral. The ad received recognition as one of that year’s top ten Super Bowl ads, without technically being a Super Bowl spot.
2. Embed advanced analytics capabilities into the “DNA” of your entire business.
- Ensure that business leaders become well-versed in data analytics; buy-in starts with the top and data has to be a part of leadership’s strategic plan.
- At P&G, supply chain had high advanced analytics capabilities while the brand teams lagged behind. Deb took on data and digital as a leadership opportunity, and it enabled her to drive massive top- and bottom-line results.
- Build an analytics mindset across the enterprise.
- Some leaders successfully embed analytics capabilities across the organization by mobilizing the bottom of the organization first. Lower levels of the business tend to adopt new tools and technology more readily, which then puts pressure on middle management (which is often more risk averse) to embrace change.
- Balance good governance, speed, and innovation.
- Create a governance model for growth – but call it “collaboration,” not “governance.”
- Leaders should actively reward collaboration across the enterprise, including teamwork between different generations.
- Younger employees tend to have an advantage because data and technology are more ingrained in their DNA and they move fast. However, these teams can also move so fast that they end up building solutions that cannot be reproduced or scaled.
- Drive collaboration by implementing 360-degree reviews. When conducting research for his book, “Shared Minds: The New Technologies of Collaboration,” Michael Schrage observed a high correlation (.7) between this HR practice and collaboration.
- Encourage virtual collaboration and informal knowledge sharing.
- A data analytics repository, using technology like Jive and Yammer, allows employees to post and tag analytics solutions, and pose questions to subject matter experts.
- Measure success by quality contributions that leverage dispersed talent.
3. Design your organization for transformation.
Decide what organizational architecture best supports the leadership’s analytics vision and maturity. Each approach has benefits and drawbacks:
- Data scientists embedded across the entire business can make a greater impact because of their proximity to businesses decisions.
- P&G’s 250 data analysts, dubbed “drivers,” sit in business spheres every week and address questions from the management team.
- Deb Henretta took this a step further. By placing a data scientist on her senior leadership team, leadership conversations benefited from the incorporation of data facts, correlations, and analytics. Additionally, participation in leadership meetings allowed data scientists to better contextualize the problems they became tasked with solving.
- Conversely, a Center of Excellence model provides the data team more freedom to focus on overarching issues and business priorities.
- Data scientists embedded into business teams also run the risk of being pulled into less strategic directions or become entrenched in lower value tasks such as managing a dashboard.
- A centralized approach fosters organic collaboration and knowledge sharing among data scientists.
4. Build the right infrastructure and IT partnership.
- IT can be more of an obstacle than a partner for many companies, especially when the majority of CIOs receive rewards for keeping systems running and may view efforts to interconnect as threatening.
- Today, data is too valuable to be siloed in IT. Data must be treated as an enterprise asset and as a corpus of knowledge to inform decisions at every level of the organization.
- Consider bringing in new players.
- Some companies employ a Chief Software Officer. Unlike the traditional CIO, a CSO focuses on the business, builds user-centric applications, and takes responsibility for continuously identifying and experimenting with data and disruptive technology.
- Build solutions that reduce the costs and latency of interoperability and integration between data sets and algorithmic techniques.
- According to Michael Schrage, “In a not too distant future, the API’s (protocols and tools to connect software) you attach to your data sets and algorithms will be as important as the data sets and algorithms themselves.”
- Doing so requires strong cooperation, collaboration, and innovation between business units, data scientists, and IT. Organizations that lack solid integration between these groups will disintermediate IT, go it alone and opt for an external cloud service like Amazon Web Services, Google Cloud Platform, or Microsoft Azure.
5. Create dashboards that tell stories.
- Visuals should operate on two levels to be successful:
- Upper level: the simplified, descriptive presentation. These uncluttered visuals focus on what matters and emphasize getting to answers.
- Lower level: the sophisticated, more complicated layer.
- It’s often hard to sell the second without the first.
- Executives often lack an operational context to understand what the dashboard is designed to communicate; they should have a view tailored to their needs, to their questions, and to how they consume data.
- “It’s not just the visualization, but the ability to create a User Experience around the visualization” – Michael Schrage.
- One participant’s company appointed a design head to Chief Visualization Officer, to focus on visualizing data in a user-friendly way.
- Effective visualizations should allow leaders to directly interact with the data – for example, testing a “what if” scenario by changing one parameter and seeing how it impacts the business.
6. Build momentum through pilots.
- Pilots can be particularly helpful for leaders at large bureaucratic organizations, providing them with the opportunity to:
- Learn firsthand what data can actually do for their organizations.
- Get an estimate of the cost of a data source or system.
- Obtain buy-in from their teams that would be using data daily and building confidence that jobs are not threatened.
- At P&G, the success of the Old Spice pilot (using data to drive decisions through a living dashboard) piqued the interest of other brands and encouraged the teams to start using data analytics more fully.
- Drive a culture of experimentation. It’s okay, and even expected, that not every pilot will achieve its objectives.
- This focus on experimentation and ‘failing fast’ creates an environment to find the disruptive change that creates the ROA promised by all the Big Data hype.
- Transformation through data analytics and digital is about leadership and culture – and it takes a top-down and bottom up approach.
- Mobilization happens through agility – you can start small and show quick wins that lead to buy-in and a cascade effect.
- Find ways to diversify access to data so that it is not siloed, whether that means collaborating with IT or disintermediating it.
Discover the key ideas and actionable insights in the full playbook: